Seasonality in Lake Champlain Copepod Thermal Limits

Copepod Collection

Copepods were collected at approximately weekly intervals from Lake Champlain (Burlington Fishing Pier). Plankton was collected from the top 3 meters using a 250 um mesh net.

# # Lake Champlain near Burlington, VT
# siteNumber = "04294500"
# ChamplainInfo = readNWISsite(siteNumber)
# parameterCd = "00010"
# startDate = "2023-01-01"
# endDate = "2024-5-20"
# #statCd = c("00001", "00002","00003", "00011") # 1 - max, 2 - min, 3 = mean
# 
# # Constructs the URL for the data wanted then downloads the data
# url = constructNWISURL(siteNumbers = siteNumber, parameterCd = parameterCd,
#                        startDate = startDate, endDate = endDate, service = "uv")
# 
# raw_temps = importWaterML1(url, asDateTime = T) %>%
#   mutate("date" = as.Date(dateTime),
#          "hour" = hour(dateTime)) %>%
#   select(dateTime, tz_cd, date, hour, degC = X_00010_00000)
# 
# temp_data =  raw_temps %>%
#   select(date, hour, "temp" = degC)
# 
# write.csv(temp_data, file = "./Output/Data/champlain_temps.csv", row.names = F)

Collections began in late May 2023. Several gaps are present, but collections have continued at roughly weekly intervals since then. Copepods from 48 collections were used to make a total of 1312 thermal limit measurements. Over this time period, collection temperatures ranged from 2.5 to 26.5°C.

There is substantial variation in thermal limits across the species collected. There is also some degree of variation within the species, with thermal limits increasing slightly during the summer.

## Daily values for the period examined by dataset
collection_conditions = temp_data %>%
  ungroup() %>% 
  group_by(date) %>% 
  summarise(mean_temp = mean(temp),
            med_temp = median(temp),
            var_temp = var(temp), 
            min_temp = min(temp), 
            max_temp = max(temp)) %>% 
  mutate("range_temp" = max_temp - min_temp,
         date = as.Date(date)) %>% 
  ungroup() %>%  
  filter(date >= (min(as.Date(full_data$collection_date)) - 7)) %>% 
  left_join(unique(select(full_data, collection_date, collection_temp)), by = join_by(date == collection_date))

## Mean female thermal limits for each species, grouped by collection
species_summaries = full_data %>%  
  #filter(sex == "female") %>% 
  group_by(sp_name, collection_date, collection_temp) %>%  
  summarise("mean_ctmax" = mean(ctmax),
            "sample_size" = n(),
            "ctmax_st_err" = (sd(ctmax) / sqrt(sample_size)),
            "ctmax_var" = var(ctmax), 
            "mean_size" = mean(size),
            "size_st_err" = (sd(size) / sqrt(sample_size)),
            "size_var" = var(size)) %>%  
  ungroup() %>% 
  complete(sp_name, collection_date) %>% 
  arrange(desc(sample_size))

adult_summaries = full_data %>%  
  filter(sex == "female") %>% 
  group_by(sp_name, collection_date, collection_temp) %>%  
  summarise("mean_ctmax" = mean(ctmax),
            "sample_size" = n(),
            "ctmax_st_err" = (sd(ctmax) / sqrt(sample_size)),
            "ctmax_var" = var(ctmax), 
            "mean_size" = mean(size),
            "size_st_err" = (sd(size) / sqrt(sample_size)),
            "size_var" = var(size)) %>%  
  ungroup() %>% 
  complete(sp_name, collection_date) %>% 
  arrange(desc(sample_size))


ggplot() + 
  geom_vline(data = unique(select(full_data, collection_date)), 
             aes(xintercept = as.Date(collection_date)),
             colour = "grey90",
             linewidth = 1) + 
  geom_line(data = collection_conditions, 
            aes(x = as.Date(date), y = mean_temp),
            colour = "black", 
            linewidth = 2) + 
  # geom_errorbar(data = species_summaries,
  #               aes(x = as.Date(collection_date),
  #                   ymin = mean_ctmax - ctmax_st_err, ymax = mean_ctmax + ctmax_st_err,
  #                   colour = sp_name),
  #               position = position_dodge(width = 1),
  #               width = 5, linewidth = 1) +
  # geom_point(data = adult_summaries, 
  #            aes(x = as.Date(collection_date), y = mean_ctmax, colour = sp_name, size = sample_size)) + 
  geom_point(data = full_data, 
             aes(x = as.Date(collection_date), y = ctmax, colour = sp_name),
             size = 2, position = position_jitter(width = 1, height = 0)) + 
  scale_colour_manual(values = species_cols) + 
  labs(x = "Date", 
       y = "Temperature (°C)", 
       colour = "Species",
       size = "Sample Size") + 
  theme_matt() + 
  theme(legend.position = "right")

lake_temps = ggplot() + 
  geom_line(data = collection_conditions, 
            aes(x = as.Date(date), y = mean_temp),
            colour = "black", 
            linewidth = 1) + 
  labs(x = "Date", 
       y = "Temperature (°C)", 
       colour = "Species",
       size = "Sample Size") + 
  theme_matt() + 
  theme(legend.position = "right")

Temperatures observed at the time of collection closely resembled the maximum daily temperature from the temperature sensor data. Maximum temperature was used as a proxy instead of mean temperature as collections were usually made during afternoons or early evenings, just following the warmest part of the day.

collection_conditions %>% 
  drop_na(collection_temp) %>%  
  ggplot(aes(x = max_temp, y = collection_temp)) + 
  geom_abline(intercept = 0, slope = 1,
              linewidth = 1, colour = "grey") + 
  geom_point(size = 3) +
  scale_x_continuous(breaks = c(5,15,25)) + 
  scale_y_continuous(breaks = c(5,15,25)) + 
  labs(x = "Max. Temp. from Sensor (°C)",
       y = "Collection Temp. (°C)") + 
  theme_matt()

Size also varied, but primarily between rather than within species.

ggplot() + 
  geom_vline(data = unique(select(full_data, collection_date)), 
             aes(xintercept = as.Date(collection_date)),
             colour = "grey90",
             linewidth = 1) + 
  geom_line(data = collection_conditions, 
            aes(x = as.Date(date), y = mean_temp),
            colour = "black", 
            linewidth = 2) + 
  # geom_errorbar(data = species_summaries,
  #               aes(x = as.Date(collection_date), 
  #                   ymin = mean_ctmax - ctmax_st_err, ymax = mean_ctmax + ctmax_st_err,
  #                   colour = sp_name),
  #               position = position_dodge(width = 1),
  #               width = 5, linewidth = 1) + 
  geom_point(data = adult_summaries, 
             aes(x = as.Date(collection_date), y = mean_size * 40, colour = sp_name, size = sample_size),
             position = position_dodge(width = 1)) + 
  scale_colour_manual(values = species_cols) + 
  scale_y_continuous(
    name = "Temperature", # Features of the first axis
    sec.axis = sec_axis(~./40, name="Prosome Length (mm)"), # Add a second axis and specify its features
    breaks = c(0,5,10,15,20,25,30)
  ) + 
  labs(x = "Date", 
       y = "Temperature (°C)", 
       colour = "Species") + 
  theme_matt() + 
  theme(legend.position = "right")

sample_dates_plot = full_data %>%  
  filter(sp_name != "Osphranticum labronectum") %>% 
  mutate(sp_name = as.factor(sp_name),
         sp_name = fct_reorder(sp_name, ctmax)) %>% 
  ggplot(aes(x = lubridate::as_date(collection_date), 
             y = sp_name, fill = sp_name)) + 
  # geom_vline(xintercept = as_date(
  #   c("2023-05-01",
  #     "2023-09-01",
  #     "2024-01-01",
  #     "2024-05-01")),
  #   colour = "grey",
  #   linewidth = 1) + 
  geom_density_ridges(bandwidth = 30,
                      jittered_points = TRUE, 
                      point_shape = 21,
                      point_size = 1,
                      point_colour = "grey30",
                      point_alpha = 0.6,
                      alpha = 0.9,
                      position = position_points_jitter(
                        height = 0.1, width = 0)) + 
  scale_fill_manual(values = species_cols) + 
  scale_x_date(date_breaks = "3 months",
               date_labels = "%b") + 
  coord_cartesian(xlim = lubridate::as_date(c("2023-04-25", "2024-06-01"))) + 
  labs(x = "Day of Year", 
       y = "Species") + 
  theme_matt() + 
  #theme_ridges(grid = T) + 
  theme(legend.position = "none",
        axis.text.x = element_text(angle = 270, hjust = 0, vjust = 0.5))

The samples captured the broad seasonal changes in calanoid copepod community composition in the lake. We note, however, that rare species (e.g. Senecella and Limnocalanus) were often preferentially sampled, so are over-represented in the data set.

adult_summaries %>% 
  ungroup() %>% 
  mutate(collection_num = as.numeric(factor(collection_date))) %>% 
  group_by(collection_date) %>%  
  arrange(collection_date) %>% 
  select(sp_name, collection_date, collection_num, sample_size) %>% 
  mutate(sample_size = replace_na(sample_size, 0)) %>% 
  mutate(total = sum(sample_size),
         percentage = sample_size / total,
         collection_date = lubridate::as_date(collection_date)) %>% 
  ggplot(aes(x = collection_date, y = percentage, fill = sp_name)) + 
  geom_area() + 
  scale_fill_manual(values = species_cols) + 
  scale_y_continuous(breaks = c(0,1)) + 
  labs(x = "Collection Date", 
       y = "Proportion", 
       fill = "Species") + 
  theme_minimal(base_size = 20) + 
  theme(panel.grid = element_blank(),
        axis.ticks = element_line())

Throughout the season, the prevalence of various unidentified pathogens also varied, with very little infection observed during the Winter and Spring.

pathogen_cols = c("no" = "grey95", "cloudy" = "honeydew3", "spot" = "antiquewhite3", "other" = "tomato3")

full_data %>% 
  select(collection_date, dev_eggs, pathogen, lipids, sp_name, sex) %>% 
  group_by() %>% 
  filter(sex != "juvenile") %>% 
  group_by(collection_date) %>% 
  count(pathogen) %>% 
  filter(pathogen != "uncertain") %>% 
  pivot_wider(id_cols = "collection_date", 
              names_from = pathogen, 
              values_from = n,
              values_fill = 0) %>% 
  mutate(total = sum(no, cloudy, spot, other)) %>% 
  pivot_longer(cols = c(no, cloudy, spot, other),
               names_to = "pathogen", 
               values_to = "count") %>% 
  mutate(percent = count/total,
         collection_date = lubridate::as_date(collection_date),
         pathogen = fct_relevel(pathogen, "no", "cloudy", "spot", "other")) %>% 
  ggplot(aes(x = collection_date, y = percent, fill = pathogen)) + 
  geom_area() + 
  scale_fill_manual(values = pathogen_cols) + 
  scale_y_continuous(breaks = c(0,1)) + 
  labs(x = "Collection Date", 
       y = "Proportion", 
       fill = "Pathogen") + 
  theme_minimal(base_size = 20) + 
  theme(panel.grid = element_blank(),
        axis.ticks = element_line())

The transparent bodies of these copepods also allowed us to examine seasonal patterns in lipid reserves and in the production of eggs. Maturing oocytes are visible in female copepods before they are released. There was no strong seasonal cycle in the production of these eggs in any species, and instead, females were reproductively active throughout their respective seasons of occurence.

dev_eggs_cols = c("no" = "grey95", "yes" = "lightblue3")

full_data %>% 
  select(collection_date, dev_eggs, pathogen, lipids, sp_name, sex) %>% 
  group_by(sp_name) %>% 
  filter(sex != "juvenile") %>% 
  group_by(sp_name, collection_date) %>% 
  count(dev_eggs) %>% 
  filter(dev_eggs != "uncertain") %>% 
  pivot_wider(id_cols = c("collection_date", "sp_name"), 
              names_from = dev_eggs, 
              values_from = n,
              values_fill = 0) %>% 
  mutate(total = sum(no, yes)) %>% 
  pivot_longer(cols = c(no, yes),
               names_to = "dev_eggs", 
               values_to = "count") %>% 
  mutate(percent = count/total,
         collection_date = lubridate::as_date(collection_date),
         dev_eggs = fct_relevel(dev_eggs, "no", "yes")) %>% 
  ungroup() %>% 
  complete(collection_date, nesting(sp_name, dev_eggs), fill = list(percent = 1)) %>% 
  mutate(percent = if_else(is.na(total) & dev_eggs == "yes", 0, percent)) %>% 
  ggplot(aes(x = collection_date, y = percent, fill = dev_eggs)) + 
  facet_wrap(sp_name~., ncol = 1) + 
  geom_area() + 
  scale_fill_manual(values = dev_eggs_cols) + 
  scale_y_continuous(breaks = c(0,1)) + 
  labs(x = "Collection Date", 
       y = "Proportion", 
       fill = "Developing \nEggs") + 
  theme_minimal(base_size = 20) + 
  theme(panel.grid = element_blank(),
        axis.ticks = element_line())

The presence of lipids varied across species, with only L. minutus, L. sicilis, and Limnocalanus regularly possessing lipid stores.

lipid_cols = c("no" = "grey95", "yes" = "sienna2")

full_data %>% 
  select(collection_date, dev_eggs, pathogen, lipids, sp_name, sex) %>% 
  group_by(sp_name) %>% 
  filter(sex != "juvenile") %>% 
  group_by(sp_name, collection_date) %>% 
  count(lipids) %>% 
  filter(lipids != "uncertain") %>% 
  pivot_wider(id_cols = c("collection_date", "sp_name"), 
              names_from = lipids, 
              values_from = n,
              values_fill = 0) %>% 
  mutate(total = sum(no, yes)) %>% 
  pivot_longer(cols = c(no, yes),
               names_to = "lipids", 
               values_to = "count") %>% 
  mutate(percent = count/total,
         collection_date = lubridate::as_date(collection_date),
         lipids = fct_relevel(lipids, "no", "yes")) %>% 
  ungroup() %>% 
  complete(collection_date, nesting(sp_name, lipids), fill = list(percent = 1)) %>% 
  mutate(percent = if_else(is.na(total) & lipids == "yes", 0, percent)) %>% 
  ggplot(aes(x = collection_date, y = percent, fill = lipids)) + 
  facet_wrap(sp_name~., ncol = 1) + 
  geom_area() + 
  scale_fill_manual(values = lipid_cols) + 
  scale_y_continuous(breaks = c(0,1)) + 
  labs(x = "Collection Date", 
       y = "Proportion", 
       fill = "Lipids\nPresent") + 
  theme_minimal(base_size = 20) + 
  theme(panel.grid = element_blank(),
        axis.ticks = element_line())

Temperature Variability

Lake Champlain is highly seasonal, with both average temperatures and temperature variability changing throughout the year. These patterns in the experienced thermal environment may drive the observed variation in copepod thermal limits. However, the time period affecting copepod thermal limits is unknown. Depending the on the duration of time considered, there are large changes in the experienced environment, in particular regarding the temperature range and variance. Consider for example three time periods: the day of collection, one week prior to collection, and four weeks prior to collection. While the overall pattern is similar, we can see that, unsurprisingly, considering longer periods of time results in larger ranges and slightly changes the pattern of variance experienced.

## Defining the function to get predictor values for periods of different lengths
get_predictors = function(daily_values, raw_temp, n_days){
  prefix = str_replace_all(xfun::numbers_to_words(n_days), pattern = " ", replacement = "-")
  
  mean_values = daily_values %>% 
    ungroup() %>% 
    mutate(mean_max = slide_vec(.x = max_temp, .f = mean, .before = n_days, .complete = T),
           mean_min = slide_vec(.x = min_temp, .f = mean, .before = n_days, .complete = T),
           mean_range = slide_vec(.x = range_temp, .f = mean, .before = n_days, .complete = T)) %>% 
    select(date, mean_max, mean_min, mean_range) %>% 
    rename_with( ~ paste(prefix, "day", .x, sep = "_"), .cols = c(-date))
  
  period_values = raw_temp %>% 
    mutate(mean = slide_index_mean(temp, i = date, before = days(n_days), 
                                   na_rm = T),
           max = slide_index_max(temp, i = date, before = days(n_days), 
                                 na_rm = T),
           min = slide_index_min(temp, i = date, before = days(n_days),
                                 na_rm = T),
           med = slide_index_dbl(temp, .i = date, .before = days(n_days), 
                                 na_rm = T, .f = median),
           var = slide_index_dbl(temp, .i = date, .before = days(n_days), 
                                 .f = var),
           range = max - min) %>%  
    select(-temp) %>%  
    distinct() %>% 
    rename_with( ~ paste(prefix, "day", .x, sep = "_"), .cols = c(-date))%>% 
    inner_join(mean_values, by = c("date")) %>%  
    drop_na()
  
  return(period_values)
}

Trait Variation

Shown below are the clutch size distributions for the three diaptomiid species, which produce egg sacs that allow for easy quantification of fecundity.

full_data %>%  
  drop_na(fecundity) %>%  
  ggplot(aes(x = fecundity, fill = sp_name_sub)) + 
  facet_wrap(.~sp_name_sub, ncol = 1) + 
  geom_histogram(binwidth = 2) + 
  scale_fill_manual(values = species_cols) + 
  labs(x = "Fecundity (# Eggs)") +
  theme_matt_facets() + 
  theme(legend.position = "none")

One of the main aims of this project is to examine the patterns and processes driving variation in upper thermal limits across these species of copepods.

Variation with temperature

We expect one of the primary drivers of copepod thermal limits to be temperature. The correlation analysis has shown that the copepods are generally (although not always) responding to the recent thermal environment. Shown below are thermal limits, body size, and fecundity values plotted against the temperature at the time of collection. Also shown is warming tolerance, calculated as the difference between upper thermal limit and the collection temperature.

We generally see an increase in thermal limits with increasing collection temperature, a slight decrease in body size, and variable relationships between temperature and fecundity. All species maintained some degree of buffer between environmental temperatures and upper thermal limits, but Epischura and L. minutus approached their upper thermal limits during the warmest collections during the summer.

ctmax_temp = ggplot(full_data, aes(x = collection_temp, y = ctmax, colour = sp_name)) +   
  geom_point(size = 3,
             alpha = 0.3) + 
  geom_smooth(method = "lm", linewidth = 3) +
  labs(x = "Collection Temperature (°C)", 
       y = "CTmax (°C)",
       colour = "Species") + 
  scale_colour_manual(values = species_cols) + 
  theme_matt() + 
  theme(legend.position = "right")

size_temp = ggplot(filter(full_data, sex != "juvenile"), aes(x = collection_temp, y = size, colour = sp_name)) + 
  geom_point(size = 3,
             alpha = 0.3) + 
  geom_smooth(method = "lm", linewidth = 3) +
  labs(x = "Collection Temperature (°C)", 
       y = "Length (mm)",
       colour = "Species")  + 
  scale_colour_manual(values = species_cols) + 
  theme_matt() + 
  theme(legend.position = "right")

wt_temp = ggplot(full_data, aes(x = collection_temp, y = warming_tol, colour = sp_name)) + 
  geom_point(size = 3,
             alpha = 0.3) + 
  geom_smooth(method = "lm", linewidth = 3) +
  labs(x = "Collection Temperature (°C)", 
       y = "Warming Tolerance (°C)",
       colour = "Species")  + 
  scale_colour_manual(values = species_cols) + 
  theme_matt() + 
  theme(legend.position = "none")

eggs_temp = ggplot(full_data, aes(x = collection_temp, y = fecundity, colour = sp_name)) + 
  geom_point(size = 3,
             alpha = 0.3) + 
  geom_smooth(method = "lm", linewidth = 3) +
  labs(x = "Collection Temperature (°C)", 
       y = "Fecundity (# Eggs)",
       colour = "Species")  + 
  scale_colour_manual(values = species_cols) + 
  theme_matt() + 
  theme(legend.position = "right")

ggarrange(ctmax_temp, size_temp, wt_temp, eggs_temp, 
          common.legend = T, legend = "right")

sp_ctmax_temp = full_data %>% 
  filter(sp_name != "Osphranticum labronectum") %>% 
  mutate(sp_name = as.factor(sp_name),
         sp_name = fct_reorder(sp_name, ctmax, .desc = T)) %>% 
  ggplot(aes(x = collection_temp, y = ctmax, colour = sp_name)) + 
  facet_wrap(sp_name~.) + 
  geom_point(size = 2, alpha = 0.2) + 
  geom_smooth(method = "lm", se = F, linewidth = 2) + 
  labs(x = "Collection Temp. (°C)", 
       y = "CTmax (°C)") + 
  scale_colour_manual(values = species_cols) + 
  theme_matt() + 
  theme(legend.position = "none")

ggarrange(sample_dates_plot, sp_ctmax_temp, nrow = 1, 
          labels = "AUTO")

Temperature dependence is relatively weak in L. sicilis, especially at cooler temperatures. We will return to this feature later in the report, but for now we will note that there are two size morphs in this species, which appear to respond differently to decreases in temperature. There are significant differences between the morphs and how temperature affects CTmax.

morph_data = full_data %>% 
  filter(sex == "female" & species == "leptodiaptomus_sicilis") %>%  mutate(sp_name = case_when(
    sp_name == "Leptodiaptomus sicilis" & size >= 0.89 ~ "Large",
    sp_name == "Leptodiaptomus sicilis" & size < 0.89 ~ "Small",
    .default = sp_name
  ))


ggplot(morph_data, aes(x = collection_temp, y = ctmax, colour = sp_name)) + 
  geom_point(size = 2, alpha = 0.8) + 
  geom_smooth(method = "lm", se = T, linewidth = 2) + 
  labs(x = "Collection Temp. (°C)", 
       y = "CTmax (°C)") + 
  theme_matt() + 
  theme(legend.position = "none")

morph.model = lm(data = morph_data, 
                 ctmax ~ collection_temp * sp_name)

knitr::kable(car::Anova(morph.model, type = "III", test = "F"))

	Sum Sq	Df	F value	Pr(>F)
(Intercept)	11838.75134	1	3930.374601	0.0000000
collection_temp	107.57172	1	35.712986	0.0000000
sp_name	32.96638	1	10.944584	0.0010357
collection_temp:sp_name	15.88294	1	5.273014	0.0222454
Residuals	1060.26547	352	NA	NA

#summary(morph.model)

morph.em = emmeans::emtrends(morph.model, "sp_name", var = "collection_temp")

knitr::kable(pairs(morph.em))

contrast	estimate	SE	df	t.ratio	p.value
Large - Small	0.1530629	0.0666562	352	2.296304	0.0222454

Copepods spent several days in lab during experiments. Shown below are the CTmax residuals (taken from a model of CTmax against collection temperature) plotted against the time spent in lab before measurements were made. Individual regressions are shown for the residuals against days in lab for each collection. We can see clearly that thermal limits are fairly stable over time.

ggplot(ctmax_resids, aes(x = days_in_lab, y = resids, colour = sp_name, group = collection_date)) + 
  facet_wrap(sp_name~.) + 
  geom_point(size = 4, alpha = 0.5) + 
  geom_smooth(method = "lm", se = F, linewidth = 1) + 
  #scale_x_continuous(breaks = c(0:5)) + 
  labs(x = "Days in lab", 
       y = "CTmax Residuals") + 
  scale_colour_manual(values = species_cols) + 
  theme_matt_facets() + 
  theme(legend.position = "none")

full.model = lme4::lmer(data = model_data,
                        ctmax ~ sex + temp_cent + size_cent + dev_eggs + lipids + pathogen + 
                          (1 + days_in_lab + temp_cent + size_cent|sp_name))

car::Anova(full.model)
## Analysis of Deviance Table (Type II Wald chisquare tests)
## 
## Response: ctmax
##             Chisq Df Pr(>Chisq)    
## sex       29.9326  2  3.164e-07 ***
## temp_cent 20.6553  1  5.498e-06 ***
## size_cent  1.9223  1     0.1656    
## dev_eggs   7.6246  2     0.0221 *  
## lipids     3.3415  2     0.1881    
## pathogen  41.4778  4  2.140e-08 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

fixed = fixef(full.model)

model_coefs = coefficients(full.model)$`sp_name` %>%  
  rownames_to_column(var = "species") %>% 
  separate(species, into = c("species"), sep = ":") %>% 
  select(species, "intercept" = "(Intercept)", temp_cent, size_cent, days_in_lab)

ggplot(model_coefs, aes(x = intercept, y = temp_cent)) + 
  geom_smooth(method = "lm", colour = "black") +
  geom_point(aes(colour = species),
             size = 6) + 
  scale_colour_manual(values = species_cols) + 
  labs(x = "Species Intercept", 
       y = "ARR") +
  theme_matt() + 
  theme(legend.position = "right")

arr_combined = synth_arr %>%
  filter(measure == "upper" & mean_lim > 20) %>% 
  select("group" = genus, arr, mean_lim) %>% 
  mutate("dataset" = "synthesis") %>% 
  bind_rows(
    select(model_coefs, "group" = species, 'arr' = temp_cent, 'mean_lim' = intercept)
  ) %>% 
  mutate(dataset = if_else(is.na(dataset), "new data", "synthesis"))


ggplot(arr_combined, aes(x = mean_lim, y = arr)) + 
  geom_smooth(method = "lm", se = F, 
              linewidth = 2, colour = "grey30") + 
  geom_point(data = filter(arr_combined, dataset != "new data"), 
             size = 4, colour = "grey") + 
  geom_point(data = filter(arr_combined, dataset == "new data"),
             aes(colour = group), 
             size = 4) + 
  scale_colour_manual(values = species_cols) + 
  labs(x = "Thermal Limit", 
       y = "ARR", 
       colour = "Species") +
  theme_matt() + 
  theme(legend.position = "none")

The term “acclimation response ratio” is often used to describe the effect of temperature on thermal limits. The ARR is calculated as the change in thermal limits per degree change in acclimation temperature. For our data, we will estimate ARR as the slope of CTmax against collection temperature. These slopes were taken from a regression of CTmax against collection temperature and body size. Two different model types were used, a simple linear regression and a mixed effects model. The estimated ARR values were generally highly similar between the model types used.

Sex and stage variation in thermal limits

Previous sections have generally lumped juvenile, female, and male individuals together. There may be important stage- or sex-specific differences in CTmax though. For several species, we have measurements for individuals in different stages or of different sexes.

sex_sample_sizes = ctmax_resids %>%  
  group_by(sp_name, sex) %>%  
  summarise(num = n()) %>%  
  pivot_wider(id_cols = sp_name,
              names_from = sex, 
              values_from = num,
              values_fill = 0) %>% 
  select("Species" = sp_name, "Juvenile" = juvenile, "Female" = female, "Male" = male)

knitr::kable(sex_sample_sizes, align = "c")

Species	Juvenile	Female	Male
Epischura lacustris	37	45	20
Leptodiaptomus minutus	12	273	38
Leptodiaptomus sicilis	31	356	95
Limnocalanus macrurus	4	43	39
Osphranticum labronectum	0	1	0
Senecella calanoides	13	21	8
Skistodiaptomus oregonensis	15	231	28

The female-male and female-juvenile comparisons show that there are generally no differences in thermal limits between these groups.

ctmax_resids %>% 
  filter(sp_name %in% filter(sex_sample_sizes, Male > 0, Female > 0)$Species & 
           sex != "juvenile") %>% 
  ggplot(aes(x = sex, y = resids, colour = sp_name, group = sp_name)) + 
  facet_wrap(sp_name~., ncol = 2) + 
  geom_smooth(method = "lm", se = F, linewidth = 1) + 
  geom_point(size = 3,
             alpha = 0.5,
             position = position_jitter(height = 0, width = 0.05)) +  
  labs(x = "Sex", 
       y = "CTmax Residuals") + 
  scale_colour_manual(values = species_cols) + 
  theme_bw(base_size = 18) + 
  theme(legend.position = "none", 
        panel.grid = element_blank())

ctmax_resids %>% 
  filter(sp_name %in% filter(sex_sample_sizes, Juvenile > 0 & Female > 0)$Species & 
           sex != "male") %>% 
  ggplot(aes(x = sex, y = resids, colour = sp_name, group = sp_name)) + 
  facet_wrap(sp_name~., ncol = 2) + 
  geom_smooth(method = "lm", se = F, linewidth = 1) + 
  geom_point(size = 3,
             alpha = 0.5,
             position = position_jitter(height = 0, width = 0.05)) +  
  labs(x = "Sex", 
       y = "CTmax (°C)") + 
  scale_colour_manual(values = species_cols) + 
  theme_bw(base_size = 18) + 
  theme(legend.position = "none", 
        panel.grid = element_blank())

Trait Correlations and Trade-offs

A relationship between size and upper thermal limits has been suggested in a wide range of other taxa. Shown below are the measured upper thermal limits plotted against prosome length. The overall relationship (inclusive of all species) is shown as the black line in the background. Regressions for each individual species are also shown. Across the entire assemblage, there is a strong decrease in thermal limits with increasing size.

full_data %>% 
  #filter(sex == "female") %>%  
  ggplot( aes(x = size, y = ctmax, colour = sp_name)) + 
  geom_smooth(data = full_data, 
              aes(x = size, y = ctmax),
              method = "lm", 
              colour ="black", 
              linewidth = 2.5) + 
  geom_point(size = 2, alpha = 0.3) + 
  geom_smooth(method = "lm", se = F, linewidth = 2) + 
  labs(x = "Length (mm)", 
       y = "CTmax (°C)",
       colour = "Species") + 
  scale_colour_manual(values = species_cols) + 
  theme_matt() + 
  theme(legend.position = "right")

Shown here is the relationship for each species individually.

full_data %>% 
  #filter(sex == "female") %>%  
  group_by(sp_name) %>% filter(n() >2) %>% filter(!str_detect(sp_name, pattern = "kindti")) %>% 
  ggplot( aes(x = size, y = ctmax, colour = sp_name)) + 
  facet_wrap(sp_name~., scales = "free", nrow = 2) + 
  geom_point(size = 2, alpha = 0.8) + 
  geom_smooth(method = "lm", se = F, linewidth = 2) + 
  labs(x = "Length (mm)", 
       y = "CTmax (°C)",
       colour = "Species") + 
  scale_colour_manual(values = species_cols) + 
  theme_matt() + 
  theme(legend.position = "none")

Shown below is the relationship between mean size and mean thermal limits for females of each species. We see that larger species within the community tend to have a lower thermal limit than smaller species.

full_data %>% 
  group_by(sp_name, sex) %>% 
  summarize(mean_ctmax = mean(ctmax, na.rm = T),
            mean_size = mean(size, na.rm = T)) %>% 
  #filter(sex == "female") %>% 
  ggplot(aes(x = mean_size, y = mean_ctmax)) + 
  geom_smooth(method = "lm", se = F, linewidth = 2, colour = "black") + 
  geom_point(aes(colour = sp_name, shape = sex),
             size = 5) + 
  labs(x = "Length (mm)", 
       y = "CTmax (°C)",
       colour = "Species") + 
  scale_colour_manual(values = species_cols) + 
  theme_matt() + 
  theme(legend.position = "right")

Shown here is the relationship between fecundity and size, showing the classic pattern of increasing egg production with increasing size.

ctmax_resids %>%  
  drop_na(fecundity) %>% 
  ggplot(aes(x = size, y = fecundity, colour = sp_name)) + 
  geom_smooth(method = "lm", se = F, linewidth = 2) + 
  geom_point(size = 2, alpha = 0.5) + 
  labs(x = "Prosome length (mm)", 
       y = "Fecundity (# Eggs)",
       colour = "Species") + 
  scale_colour_manual(values = species_cols) + 
  theme_matt() + 
  theme(legend.position = "right")

Individuals may also allocate energy to different fitness related traits, prioritizing reproductive output over environmental tolerance, for example. Shown below is the relationship between CTmax residuals (again, controlling for the effects of collection temperature) against fecundity. We can see clearly that individuals with increased fecundity are not decreasing thermal limits, suggesting that there is no energetic trade-off between these traits.

ctmax_resids %>%  
  drop_na(fecundity) %>% 
  ggplot(aes(x = resids, y = fecundity_resids, colour = sp_name)) + 
  geom_smooth(method = "lm", se = F, linewidth = 2) + 
  geom_point(size = 2, alpha = 0.5) + 
  labs(x = "CTmax Residuals", 
       y = "Fecundity Residuals",
       colour = "Species") + 
  scale_colour_manual(values = species_cols) + 
  theme_matt() + 
  theme(legend.position = "right")

Other patterns in variation

Leptodiaptomus sicilis is the most abundant species during the winter. There was a large shift in the size of mature females towards the end of December. These large and small individuals are the same species (confirmed via COI sequencing), suggesting this shift may reflect a transition from one generation to another and that, unlike in many other lakes, there are two generations of L. sicilis per year in Lake Champlain. This size difference may be caused by differences in the developmental environments. For example, individuals developing in January grow up at very low temperatures, and therefore may reach larger sizes. These individuals oversummer in deep waters, then re-emerge in October and produce a new generation. Water temperatures are still fairly high through November, which results in a generation of smaller individuals, which mature in time to produce a new generation in January.

Shown below is the distribution of pairwise distances between COI sequences of large and small morphs. Distances in both within- and across-morph comparisons are small.

ind_dist = ape::dist.dna(sic_dnabin, model = "raw") %>% as.matrix %>% 
  as_tibble() %>%
  mutate("ind1" = colnames(.)) %>% 
  pivot_longer(-ind1, names_to = "ind2", values_to = "dist") %>%
  mutate(ind1 = factor(ind1),
         ind2 = factor(ind2)) %>% 
  filter(!(ind1 == "sore1" | ind2 == "sore1")) %>% 
  mutate(
    ind1 = case_when(
      ind1 == "S1" ~ "small1",
      ind1 == "S3" ~ "small3",
      ind1 == "lsic3" ~ "small4",
      ind1 == "lsic5" ~ "small6",
      ind1 == "lsic9" ~ "small8",
      ind1 == "lsic10" ~ "small9",
      ind1 == "lsic11" ~ "small10",
      ind1 == "L1" ~ "large1",
      ind1 == "L2" ~ "large2",
      ind1 == "L3" ~ "large3",
      ind1 == "lsic1" ~ "large4",
      ind1 == "lsic2" ~ "large5",
      ind1 == "lsic7" ~ "large6",
      ind1 == "lsic8" ~ "large7"),
    ind2 = case_when(
      ind2 == "S1" ~ "small1",
      ind2 == "S3" ~ "small3",
      ind2 == "lsic3" ~ "small4",
      ind2 == "lsic5" ~ "small6",
      ind2 == "lsic9" ~ "small8",
      ind2 == "lsic10" ~ "small9",
      ind2 == "lsic11" ~ "small10",
      ind2 == "L1" ~ "large1",
      ind2 == "L2" ~ "large2",
      ind2 == "L3" ~ "large3",
      ind2 == "lsic1" ~ "large4",
      ind2 == "lsic2" ~ "large5",
      ind2 == "lsic7" ~ "large6",
      ind2 == "lsic8" ~ "large7"),
    'comparison' = case_when(
      str_detect(ind1, pattern = "large") & str_detect(ind2, pattern = "large") ~ "within",
      str_detect(ind1, pattern = "small") & str_detect(ind2, pattern = "small") ~ "within", 
      str_detect(ind1, pattern = "large") & str_detect(ind2, pattern = "small") ~ "across",
      str_detect(ind1, pattern = "small") & str_detect(ind2, pattern = "large") ~ "across"
    )) 

ggplot(ind_dist, aes(dist, fill = comparison)) +
  geom_histogram(binwidth = 0.005) + 
  labs(x = "Distance") + 
  theme_matt()

full_data %>%  
  filter(sp_name == "Leptodiaptomus sicilis") %>% 
  filter(sex != "juvenile") %>% 
  group_by(collection_date) %>% 
  mutate(size_center = scale(size, center = T, scale = F)) %>% 
  ggplot(aes(y = factor(collection_date), x = size, fill = collection_temp)) + 
  facet_wrap(sex~.) + 
  geom_density_ridges(bandwidth = 0.04) + 
  geom_vline(xintercept = 0.89) + 
  labs(x = "Size (mm)",
       y = "Date", 
       fill = "Coll. Temp. (°C)") + 
  theme_matt() + 
  theme(legend.position = "right",
        axis.text.y = element_text(size = 12))

full_data %>%  
  filter(sp_name == "Leptodiaptomus minutus") %>% 
  filter(sex != "juvenile") %>% 
  ggplot(aes(y = factor(collection_date), x = size, fill = collection_temp)) + 
  facet_wrap(sex~.) + 
  geom_density_ridges(bandwidth = 0.04) + 
  geom_vline(xintercept = 0.69) + 
  labs(x = "Size (mm)",
       y = "Date", 
       fill = "Coll. Temp. (°C)") + 
  coord_cartesian(xlim = c(0.5,0.9)) + 
  theme_matt() + 
  theme(legend.position = "right",
        axis.text.y = element_text(size = 12))

Distribution Lag Non-Linear Model (DLNM approach)

Distributed lag models examine a response variable, measured at multiple time points, as a function of the lagged occurrence of some predictor variable (response y at time t as a function of preditor x(t-lag). This method utilizes a bi-dimensional dose-lag-response function, which essentially examines not only the dose effect, but the effect of the timing of the dose.

# 
# lag_temps = temp_data %>%
#   group_by(date, hour) %>%
#   summarize("mean_temp" = mean(temp, na.rm = T)) %>%
#   ungroup() %>%
#   mutate(point_num = row_number())
# 
# uniq_days = length(unique(lag_temps$date))
# 
# g = gam(mean_temp ~ s(point_num, bs="cr", k=uniq_days + 10),
#     method = "REML",
#     data = lag_temps)
# 
# points = seq(1, nrow(lag_temps), length.out = length(lag_temps$hour))
# 
# df.res = df.residual(g)
# 
# pred_temps = predict(g, newdata = lag_temps, type = "response", se.fit = TRUE)
# 
# lag_temps = lag_temps %>%
#   mutate(trend_T = pred_temps$fit,
#          trend_se = pred_temps$se.fit,
#          temp_diff = mean_temp - trend_T)
# 
# write.csv(lag_temps, file = "./Output/Data/lag_temps.csv", row.names = F)

dlnm_data = full_data %>%  
  filter(sex == "female") %>% 
  filter(sp_name %in% c(
    "Leptodiaptomus sicilis",
    "Leptodiaptomus minutus",
    "Skistodiaptomus oregonensis"
  )) %>% 
  select(collection_date, collection_temp, sp_name, ctmax) %>% 
  group_by(collection_date, collection_temp, sp_name) %>%  
  summarise(mean_ctmax = mean(ctmax, na.rm = T),
            sample = n())

temp_data %>% 
  group_by(date) %>% 
  summarise(mean_temp = mean(temp)) %>% 
  right_join(dlnm_data, by = join_by("date" == "collection_date")) %>% 
  ggplot(aes(x = mean_temp, y = mean_ctmax)) + 
  facet_wrap(.~sp_name) + 
  geom_smooth(method = "gam") + 
  geom_point() + 
  labs(x = "Mean Daily Temp. (°C)",
       y = "Mean CTmax (°C)") + 
theme_matt_facets() + 
  theme(strip.text.x = element_text(size = 12))

sp_list = unique(dlnm_data$sp_name)

for(lag_species in sp_list){
  
  dlnm_data_sp = dlnm_data %>% 
    filter(sp_name == lag_species)
  
  # We need to estimate a matrix of exposure histories for each observation. This contains the series of exposures at each lag (l) for each of the n observations, constrained between l0 (minimum lag) and L (max lag). 

dates = dlnm_data_sp$collection_date # For each of these dates, make a vector of the past 30 days (including the day of collection). NOTE: Don't use 'unique' dates here since some collections had multiple species

exp_hist_z = data.frame()
exp_hist_trend = data.frame()

for(d in dates){
  
  history = lag_temps %>% 
    filter(date <= d & date > d - 31) %>% 
    arrange(desc(date), desc(hour)) %>% 
    mutate(lag = row_number() - 1) %>% 
    select(lag, mean_temp, temp_diff)
  
  z_vec = scale(history$mean_temp)[,1]
  names(z_vec) = history$lag
  
  trend_vec = history$temp_diff
  names(trend_vec) = history$lag

  exp_hist_z = bind_rows(exp_hist_z, z_vec)
  exp_hist_trend = bind_rows(exp_hist_trend, trend_vec)
  
}

#print(max(exp_hist_trend, na.rm = T))

# The cross-basis function from dlnm will use the class of the x parameter to determine what to do. In our case, we need to provide it with the matrix of exposure histories for reach observation (row) and lag (column). 

cb_temps = crossbasis(exp_hist_trend, lag = c(0,dim(exp_hist_trend)[2]-1), 
                      argvar =list(fun="cr",df=3), 
                      arglag=list(fun="cr",df=3,intercept=T))

#summary(cb_temps)

penalized_mat <- cbPen(cb_temps)

#fitting GAM
lag.gam = gam(data = dlnm_data_sp, 
              mean_ctmax ~ cb_temps + collection_temp, 
              method = "GCV.Cp",
              paraPen=list(cb_temps=penalized_mat))

# summary(lag.gam)
# AIC(lag.gam)

#estimation of exposures effects

#default plots
pred_gam_Zs<-crosspred(cb_temps, lag.gam, 
                       cumul=F, cen=0, ci.level = 0.95,
                       at=seq(-4,4, 0.1))

plot(pred_gam_Zs, "contour")

plot(pred_gam_Zs, border = 2, cumul=F,
      theta=110,phi=20,ltheta=-80)

plot(pred_gam_Zs, "slices", 
     var = c(3,-3), 
     lag = c(1,336), 
     col = 2)

}

if(predict_vuln == F){
  knitr::knit_exit()
}